{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "672ee0e6",
   "metadata": {},
   "source": [
    "## 🔘 Create dummy data for `Clustering`\n",
    "\n",
    "`sklearn.datasets.make_blobs()` Generates isotropic Gaussian blobs for clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e343277",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b4791b",
   "metadata": {},
   "source": [
    "**Make Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1104be50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the features (X) and output (y) with 100 samples,\n",
    "X, y = make_blobs(n_samples = 100,\n",
    "                  n_features = 2, # dimension of each data point,\n",
    "                  centers = 3, # number of clusters,\n",
    "                  cluster_std = 0.5, # std deviation of each cluster\n",
    "                 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81072c52",
   "metadata": {},
   "source": [
    "**Plot Blobs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f7785d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df3f68aa",
   "metadata": {},
   "source": [
    "🔘 Hope you enjoyed reading!! 📖 <br>\n",
    "🔘 follow → `@akshay_pachaar`  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "694ef174",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_twitter",
   "language": "python",
   "name": "env_twitter"
  },
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